AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior

Autores
Sabattini, Julian Alberto; Sturniolo, Francisco; Bollazzi, Martín; Bugnon, Leandro Ariel
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.
Fil: Sabattini, Julian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Sturniolo, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Bollazzi, Martín. Universidad de la Republica; Uruguay
Fil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; Argentina
Materia
ANT TRACKING
DEEP LEARNING
IMAGE PROCESSING
LEAF-CUTTER ANTS
TRACKING IN THE WILD
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/223606
Acceder
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network_name_str CONICET Digital (CONICET)
spelling AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behaviorSabattini, Julian AlbertoSturniolo, FranciscoBollazzi, MartínBugnon, Leandro ArielANT TRACKINGDEEP LEARNINGIMAGE PROCESSINGLEAF-CUTTER ANTSTRACKING IN THE WILDhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.Fil: Sabattini, Julian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Sturniolo, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Bollazzi, Martín. Universidad de la Republica; UruguayFil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; ArgentinaElsevier2023-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/223606Sabattini, Julian Alberto; Sturniolo, Francisco; Bollazzi, Martín; Bugnon, Leandro Ariel; AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior; Elsevier; Smart Agricultural Technology; 5; 100252; 10-2023; 1-72772-3755CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2772375523000825info:eu-repo/semantics/altIdentifier/doi/10.1016/j.atech.2023.100252info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:16Zoai:ri.conicet.gov.ar:11336/223606instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:16.919CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
title AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
spellingShingle AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
Sabattini, Julian Alberto
ANT TRACKING
DEEP LEARNING
IMAGE PROCESSING
LEAF-CUTTER ANTS
TRACKING IN THE WILD
title_short AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
title_full AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
title_fullStr AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
title_full_unstemmed AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
title_sort AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
dc.creator.none.fl_str_mv Sabattini, Julian Alberto
Sturniolo, Francisco
Bollazzi, Martín
Bugnon, Leandro Ariel
author Sabattini, Julian Alberto
author_facet Sabattini, Julian Alberto
Sturniolo, Francisco
Bollazzi, Martín
Bugnon, Leandro Ariel
author_role author
author2 Sturniolo, Francisco
Bollazzi, Martín
Bugnon, Leandro Ariel
author2_role author
author
author
dc.subject.none.fl_str_mv ANT TRACKING
DEEP LEARNING
IMAGE PROCESSING
LEAF-CUTTER ANTS
TRACKING IN THE WILD
topic ANT TRACKING
DEEP LEARNING
IMAGE PROCESSING
LEAF-CUTTER ANTS
TRACKING IN THE WILD
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.
Fil: Sabattini, Julian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Sturniolo, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Bollazzi, Martín. Universidad de la Republica; Uruguay
Fil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; Argentina
description Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.
publishDate 2023
dc.date.none.fl_str_mv 2023-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/223606
Sabattini, Julian Alberto; Sturniolo, Francisco; Bollazzi, Martín; Bugnon, Leandro Ariel; AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior; Elsevier; Smart Agricultural Technology; 5; 100252; 10-2023; 1-7
2772-3755
CONICET Digital
CONICET
url http://hdl.handle.net/11336/223606
identifier_str_mv Sabattini, Julian Alberto; Sturniolo, Francisco; Bollazzi, Martín; Bugnon, Leandro Ariel; AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior; Elsevier; Smart Agricultural Technology; 5; 100252; 10-2023; 1-7
2772-3755
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2772375523000825
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.atech.2023.100252
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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